Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes

Nat Methods. 2020 Jun;17(6):615-620. doi: 10.1038/s41592-020-0820-1. Epub 2020 May 4.


Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Base Sequence
  • Cell Line
  • Cluster Analysis
  • Genotype
  • Humans
  • Polymorphism, Single Nucleotide
  • RNA / genetics*
  • RNA-Seq / methods*
  • Sensitivity and Specificity
  • Single-Cell Analysis / methods*
  • Software


  • RNA